oores Analytics® Training
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Machine Learning in R
- Live Via Zoom
- Course Fee: Starting at $250 depending on Country of participation (Limited Scholarships and variable pricing available)
Learning Objectives
By the end of the course, students will be able to:
- Apply machine learning workflows in R for real-world datasets
- Build, tune, and evaluate predictive and unsupervised models.
- Implement feature engineering and interpret model outputs.
- Communicate findings using reproducible R Markdown or Shiny dashboards.
- Design and present an end-to-end ML project.
Using R for Data management and Reporting
- Live Via Zoom
- Course Fee: Starting at $250 depending on Country of participation (Limited Scholarships and variable pricing available)
Learning Objectives
By the end of the course, students will be able to:
- Import, clean, and manage data from multiple file formats in R
- Apply tidy data principles to restructure datasets for analysis
- Handle missing, inconsistent, and duplicate data effectively
- Create reproducible and well-documented data management workflows
- Validate and prepare datasets for analysis, reporting, or sharing
- Produce elegant figures and tables summarizing a dataset.
- Produce a polished report using RMarkdown or R Notebooks.
- Write custom functions in R
- Basic modeling through R
Machine Learning for Survival Analysis in R
- Live Via Zoom
- Course Fee: Starting at $250 depending on Country of participation (Limited Scholarships and variable pricing available)
Learning Objectives
By the end of the course, students will be able to:
- Understand and apply core survival analysis concepts such as censoring, hazards, and time-to-event risk
- Build and interpret Kaplan–Meier and Cox proportional hazards models in R
- Apply machine learning methods for survival data, including penalized Cox models, Random Survival Forests, boosting, and deep learning approaches
- Evaluate and compare survival models using C-index, Brier score, calibration, and time-dependent AUC
- Use tidymodels and modern R workflows to create reproducible survival ML pipelines
- Interpret and explain model results using variable importance and explainability tools
- Communicate survival ML insights clearly to public health, clinical, and policy audiences
Research Methods for Social Science & Public Health
- Live Via Zoom
- Course Fee: Starting at $250 depending on Country of participation (Limited Scholarships and variable pricing available)
Learning Objectives
By the end of the course, students will be able to:
- Translate a substantive research question into a testable research design appropriate to the question's causal and descriptive demands
- Distinguish experimental, quasi-experimental, and observational designs and identify the threats to validity specific to each
- Design defensible sampling strategies and justify sample size using power considerations, not convention
- Operationalize abstract constructs into measurable variables and evaluate instruments for reliability and validity
- Critically evaluate published quantitative research for confounding, selection bias, and misreported uncertainty
- Draft a methodologically sound research proposal appropriate for IRB submission, grant application, or thesis/dissertation proposal
LLM Agents for Data Science and Research
- Live Via Zoom
- Course Fee: Starting at $250 depending on Country of participation (Limited Scholarships and variable pricing available)
Learning Objectives
By the end of the course, students will be able to:
- Interact with LLMs programmatically in R using the ellmer package, including structured data extraction from unstructured text into tidy data frames.
- Design and register custom tools that let an LLM call real R functions, building agents that compute real answers instead of generating plausible guesses
- Build a retrieval-augmented generation (RAG) pipeline - chunking, embedding, and retrieving source documents - to ground LLM answers in real evidence with citations.
- Chain multiple agent capabilities (retrieval, extraction, computation) into a multi-step research workflow with self-correction and factuality checks
- Produce a reproducible, cited research report generated by an autonomous agent, suitable for a literature review or evidence synthesis task.
Dashboard Data Visualization in R
- Live Via Zoom
- Course Fee: Starting at $250 depending on Country of participation (Limited Scholarships and variable pricing available)
Learning Objectives
By the end of the course, students will be able to:
- Build interactive charts and linked visualizations using plotly, DT, and crosstalk that work without a Shiny server.
- Design and publish a Quarto dashboard combining KPI value boxes, charts, and tables in a shareable, static HTML layout.
- Apply Shiny's reactive programming model to build dashboards that respond to user input in real time.
- Combine plotly, DT, and crosstalk inside a Shiny app with bslib theming to produce a polished, production-grade interface.
- Deploy an interactive dashboard to a live environment (e.g., shinyapps.io) that a non-technical stakeholder can use without opening R.
Spatial Data Visualization in R
- Live Via Zoom
- Course Fee: Starting at $250 depending on Country of participation (Limited Scholarships and variable pricing available)
Learning Objectives
By the end of the course, students will be able to:
- Represent, manipulate, and visualize vector spatial data (points, lines, polygons) in R using the sf package, including coordinate reference systems and spatial joins
- Produce publication-quality static choropleth and thematic maps using ggplot2's grammar of graphics, including proper map composition elements
- Build interactive maps with leaflet and tmap, including clustering, layering, and popups for exploratory and public-facing use.
- Work with raster data in R using terra, including resampling, zonal statistics, and multi-band composites
- Apply spatial statistics (e.g., Moran's I, LISA cluster maps) and deploy a spatial dashboard combining vector and raster data.